Computer generated responses to user input such as dialogue, images, and the like, are often limited in diversity and/or not particularly relevant to the user input. For example, computer generated responses to user input such as dialogue in conventional systems may include phrases such as “I don't know,” “I'm sorry,” and “I don't know what you are talking about,” that are safe, limited in diversity and not particularly relevant to the topic of the conversation. In another example, computer generated responses to user input such as images containing stop signs in conventional systems may generate images that are safe such as images containing red signs, rather than ones that are particularly relevant.
While advances in machine learning, especially within deep neural networks, have enabled new capacity for machines to learn behavior from repository human behavioral data, existing neural network architecture and/or methodology are still limited in extracting valuable information from these large amounts of data, and continue to produce computer generated responses to user input that are limited in diversity and/or not particularly relevant to the topic of the user input (i.e., dialogue, images).